Comparative Study of Tomato Crop Disease Detection System Using Deep Learning Techniques

Abstract

Agriculture is the most important element of any country in several ways. The growth in agriculture helps to improve the country’s economy. Today, AgriTech is a growing field in the world that helps to improve the crop quality and quantity. Using different advanced techniques, farmers can be benefited. So many challenges are faced by the farmers during crop production. Crop disease is one of the most difficult obstacle of agriculture field. Many advanced techniques such as deep learning methods have been introduced to detect the crop diseases. Some convolutional neural network (CNN) architectures used for tomato crop disease detection are discussed in this paper. Comparative study of different CNN models like AlexNet, GoogleNet, ResNet, UNet, and SqueezNet has been performed.